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Camera-Based ASL Alphabet Recognition Using Hand Landmark Features 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Chakravarty, Anwesha Choudhary, Ayesha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Sign language is the fundamental mode of communication for people with hearing and speaking disabilities worldwide. The goal of automating sign language understanding is to establish inclusive visual communication to facilitate conversations between sign language users and the wider community. This paper deals with the problem of understanding ASL alphabet gestures and proposes a two-stage deep learning and computer vision-based framework for ASL alphabet recognition and classification into 26 distinct classes. Existing systems struggle with variations in signer appearance, complex backgrounds and limited high-quality data. The proposed method attempts to address these challenges by experimenting with features and classification techniques and uses a learning-based automatic annotation method for labelling data. This work contributes to the ongoing effort of building automated camera-based systems for inclusive communication that can potentially revolutionize visual communication.

关键词： Sign Language Recognition ASL Keypoint Extraction

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Bridging Ethics and Code: A PBL Framework for Digital Citizenship in CS 17th

Bridging Ethics and Code: A PBL Framework for Digital Citize...

引用

17th India Compute Conference-COMPUTE-Annual

作者： Rana, Pratik Plaksha Univ Mohali Punjab India

ISBN: (纸本)9783031843907;9783031843914

As technology increasingly permeates society, integrating digital citizenship concepts into computer science (CS) education has become crucial. This paper presents a novel framework that seamlessly merges digital citizenship principles with project-based learning (PBL) in CS curricula. The framework emphasizes competencies such as digital literacy, ethical decision-making, and online safety, fostering the development of responsible innovators capable of addressing the societal implications of their work. By aligning digital citizenship themes with relevant CS topics, the framework encourages critical thinking and collaborative problem-solving while providing hands-on learning experiences. The paper outlines specific integration strategies for various CS subjects and proposes comprehensive assessment methods to evaluate both ethical reasoning and technical proficiency. Case studies demonstrate the frameworks practical application, showcasing its effectiveness in cultivating a holistic understanding of technical skills and ethical considerations. This approach aims to empower the next generation of computer scientists to engage in responsible innovation, ensuring ethical practices are at the forefront of technological advancement. The paper concludes by discussing potential evaluation methods for future research and exploring the broader implications of this integrated approach for CS education and the technology industry.

关键词： Digital citizenship Project-based learning computer science education Ethical computing Curriculum integration STEM ethics Technology literacy Responsible innovation

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Ensemble Tidal Prediction Scheme by Combining Harmonic Analysis and Meteorological Predictive Module 5th

Ensemble Tidal Prediction Scheme by Combining Harmonic Analy...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Rui Yin, Jianchuan Xu, Dongxing Guangdong Ocean Univ Zhanjiang 524009 Peoples R China Guangdong Prov Key Lab Intelligent Equipment Sout Zhanjiang 524088 Peoples R China Guangdong Prov Engn Res Ctr Ship Intelligence & S Zhanjiang 524088 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Attributing to the influences of complex environmental factors on tides, there are often large errors in tidal prediction. To improve the accuracy of tide prediction in practical application, we apply the multi-module combinatorial prediction method to tidal prediction by taking consideration of the correlation between tidal level changes and air pressure, air temperature. By jointly modeling air pressure and air temperature data and tidal data, the empirical mode decomposition (EMD) method can extract features that affecting dynamic tidal changes. The harmonic constant method is firstly employed to predict the periodical changes of tides, then the air pressure and air temperature data are processed by the EMD method to extract the features that affecting the dynamic tidal changes. Then, the difference between the predicted value by harmonic method and the measured values was decomposed at multiple levels, and the long and short-term memory network (LSTM) model was used to predict the dynamic tidal changes between the measured tidal level and the periodical changes predicted by harmonic method. In the model, the effects of air pressure and air temperature on tides are considered and adjusted by the model parameters adaptively to accommodate the effects of complex environmental factors on tides. Finally, the tidal prediction results of the harmonic constant model are combined with those of the modular method. Tidal prediction simulation was conducted based on the Tidal stations of Canaveral and Old Port Tampa, the results show that the multi-module combination prediction method exhibits better performance than the traditional harmonic analysis method, providing a new approach for tidal prediction in areas of Marine safety management, Marine science research, etc.

关键词： Harmonic constant method Ensemble prediction method Correlation analysis Adaptive module Adjustment Long short-term memory network

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Optimized Brain Tumor Segmentation for Resource Constrained Settings: VGG-Infused U-Net Approach 1st

Optimized Brain Tumor Segmentation for Resource Constrained ...

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1st MICCAI Meets Africa Workshop

作者： Degu, Mizanu Zelalem Raymond, Confidence Zhang, Dong Saleh, Amal Anazodo, Udunna C. Simegn, Gizeaddis Lamesgin Jimma Univ Fac Comp & Informat Jimma Ethiopia Med Artificial Intelligence Lab MAI Lab Lagos Nigeria Lawson Hlth Res Inst London ON Canada Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Addis Ababa Univ Sch Med Coll Hlth Sci Addis Ababa Ethiopia McGill Univ Montreal Neurol Inst Montreal PQ Canada Univ Cape Town Dept Clin & Radiat Oncol Cape Town South Africa Jimma Univ Sch Biomed Engn Jimma Ethiopia

ISBN: (纸本)9783031791024;9783031791031

Semantic segmentation of brain tumors plays a crucial role in assisting treatment by precisely delineating tumor boundaries in brain images. This aids clinicians in formulating surgical plans and targeted therapies, ultimately enhancing patient outcomes. Automatic brain tumor segmentation poses a significant challenge due to its complexity and resource-intensive nature, particularly in low-resource settings where access to high quality data and expertise is limited, hindering effective medical image analysis and treatment planning. In this study, the complex multi-label 3D segmentation problem was simplified into more manageable 2D single-label segmentation tasks and a U-Net architecture was adapted, with the integration of a pre-trained VGG19 model to reduce computational demands while effectively extracting features. Six models were trained in total, three using the conventional U-Net and three employing the VGG-infused U-Net (VIU-Net). The Experiment was performed using a total of 1424 magnetic resonance images. The study shows that the VIU-Net model achieved lower dice losses of 0.188, 0.068, and 0.106 for the segmentation of Necrotic Core, SNFH, and Enhancing Tumor regions respectively, showcasing the effectiveness of our method and it's suitability for implementation in low resource settings.

关键词： Automatic Segmentation Brain Tumor Optimized U-Net Low Resource Setting

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Restating the Prowess of Logic Programming 8th

Restating the Prowess of Logic Programming

引用

2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Yang, Feng-Jen Florida Polytechn Univ Lakeland FL 33805 USA

ISBN: (纸本)9783031856273;9783031856280

Logic programming stands as a cornerstone among programming paradigms, presenting an unparalleled methodology for addressing a spectrum of quintessential AI challenges. This paradigm harnesses the power of predicate logic to enhance the inference mechanism, thereby demonstrating exceptional proficiency in complex problem-solving domains, including graph traversal and constraint satisfaction dilemmas. This paper explores the potent application of logic programming in devising innovative solutions for the classic conundrum of the farmer-wolf-chicken-grain riddle, showcasing its robustness and versatility in computational reasoning.

关键词： Logic Programming Declarative Logic Artificial Intelligence

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SNN-CPG Hierarchical Control Enhanced Motion Performance of Robotic Fish Based on STDP 5th

SNN-CPG Hierarchical Control Enhanced Motion Performance of ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zuo, Lingchen Wang, Ming Gong, Yanling Wang, Ruilong Zhao, Qianchuan Zheng, Xuehan Gao, He Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Zhengchen Technol CO LTD Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

A bionic robotic fish is a fish-shaped robot that exhibits exceptional movement efficiency, rapid swimming speed, and impressive maneuverability. Nevertheless, the motion control of robotic fish is exclusively based on oscillatory motion in water, which lacks biological reasoning and interpretability in motion control, thus unable tomeet the demands for high motion efficiency and adaptability. This work proposes a hierarchical motion control method aimed at enhancing the motion efficiency of robotic fish. The method utilizes spiking neural network (SNN) and central pattern generator (CPG) to optimize the control system, and introduces Spike-Timing-Dependent Plasticity (STDP) learning rules to adjust synaptic weights, thereby enhancing the model's adjustability and learning ability to better mimic synaptic plasticity between biological neurons. The established hierarchical control strategy is compared and verified in terms of speed, stability, and action fluency through simulations and experiments of different control methods under different spiking neuron topologies. To summarize, this research offers a scientifically feasible alternative for the motion control of robotic fish.

关键词： Robotic Fish SNN CPG Hierarchical Control Motion Control

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Neural Network-Based Vehicular Channel Estimation Performance: Effect of Noise in the Training Set 5th

Neural Network-Based Vehicular Channel Estimation Performanc...

引用

5th Southern African Conference for Artificial Intelligence Research

作者： Ngorima, Simbarashe Aldrin Helberg, Albert S. J. Davel, Marelie H. Northwest Univ Fac Engn Potchefstroom South Africa Ctr Artificial Intelligence Res Cape Town South Africa Natl Inst Theoret & Computat Sci Stellenbosch South Africa

ISBN: (纸本)9783031782541;9783031782558

Vehicular communication systems face significant challenges due to high mobility and rapidly changing environments, which affect the channel over which the signals travel. To address these challenges, neural network (NN)-based channel estimation methods have been suggested. These methods are primarily trained on high signal-to-noise ratio (SNR) with the assumption that training a NN in less noisy conditions can result in good generalisation. This study examines the effectiveness of training NN-based channel estimators on mixed SNR datasets compared to training solely on high SNR datasets, as seen in several related works. Estimators evaluated in this work include an architecture that uses convolutional layers and self-attention mechanisms;a method that employs temporal convolutional networks and data pilot-aided estimation;two methods that combine classical methods with multilayer perceptrons;and the current state-of-the-art model that combines Long-Short-Term Memory networks with data pilot-aided and temporal averaging methods as post processing. Our results indicate that using only high SNR data for training is not always optimal, and the SNR range in the training dataset should be treated as a hyperparameter that can be adjusted for better performance. This is illustrated by the better performance of some models in low SNR conditions when trained on the mixed SNR dataset, as opposed to when trained exclusively on high SNR data.

关键词： Channel estimation deep learning neural networks CNN-Transformer IEEE 802.11p vehicular channels

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IoT System Intrusion Detection with XGBoost Optimized by Modified Metaheuristics 4th

IoT System Intrusion Detection with XGBoost Optimized by Mod...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Ivanovic, Stefan Zivkovic, Miodrag Antonijevic, Milos Perisic, Jasmina Jovanovic, Luka Dedic, Velimir Bacanin, Nebojsa Singidunum Univ Danijelova 32 Belgrade 11010 Serbia Union Nikola Tesla Univ Fac Informat Technol & Engn Belgrade Serbia

ISBN: (纸本)9783031837821;9783031837838

Intrusion detection in Internet of Things (IoT) environments represents a crucial aspect of keeping interconnected devices, networks, and data secure from unauthorized access and malicious activities. With IoT's fast growth in applications ranging from smart homes to industry and healthcare, securing these devices became intrinsic due to their resource-limited nature and diverse architectures. This study examines intrusion detection in IoT networks utilizing XGBoost classifier. A novel version of chimp optimization algorithm has been introduced to execute optimization of XGBoost classifier's hyperparameters to improve classification capabilities of the model for intrusion detection in IoT systems. Extensive comparative analysis was conducted where the suggested approach was compared against several potent optimizers used in the identical setup. Simulation outcomes indicate superior results of the proposed approach, suggesting its great potential in this particular domain.

关键词： Intrusion detection XGBoost Metaheuristics optimization Chimp optimization algorithm Swarm intelligence

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Exploiting Attackability for Effective Textual Adversarial Attacks 4th

Exploiting Attackability for Effective Textual Adversarial A...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Khemis, Salim Yacine, Amara Akrem, Benatia Mohamed Ecole Mil Polytech EMP BP 17 Bordj El Bahri Algiers 16046 Algeria

ISBN: (纸本)9783031821493;9783031821509

Deep learning models have achieved remarkable success across various domains, yet their susceptibility to adversarial attacks remains a pressing concern. While recent advancements in adversarial attacks have aimed to enhance model defenses, many existing techniques suffer from drawbacks such as higher perturbation rates, higher query count, reduced textual similarity, or lower success rates. This paper addresses this problematic by proposing a dynamic search strategy that leverages the concept of attackability to guide and optimise the generation of adversarial attacks. The method seeks to improve the quality of generated adversarial samples via minimizing perturbation rates, query count, and maintaining high success rates. Experimental results demonstrate its effectiveness compared to existing techniques, representing a significant advancement in the field of adversarial attack generation.

关键词： Adversarial attacks and defenses Natural Language Processing (NLP) Attackability Deep learning robustness

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Securing Vehicular Ad-Hoc Networks: A Blockchain and Software Defined Network Approach for Enhanced Efficiency, Safety, and Security 2nd

Securing Vehicular Ad-Hoc Networks: A Blockchain and Softwar...

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2nd International Conference on information Technologies and Their Applications

作者： Ajesh, F. Philip, Felix M. Triwiyanto, T. Shafi, Danyalov Sabir, Mammadov Abuzarova, Vusala Aliyeva, Samira Khurshudov, Dursun Abdullayev, Vugar Hacimahmud Mikailzade, Latafat Asgarov, Taleh Sree Buddha Coll Engn Dept Comp Sci & Engn Alappuzha Kerala India Jain Deemed Univ Dept Comp Sci & Informat Technol Bangalore Karnataka India Poltekkes Kemenkes Surabaya Dept Med Elect Technol Surabaya Indonesia Azerbaijan Univ Architecture & Construct Baku Azerbaijan Azerbaijan State Oil & Ind Univ Dept Comp Engn Baku Azerbaijan Azerbaijan Natl Aviat Acad Dept Aerosp Informat Syst Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

The Vehicular Ad-Hoc Network (VANET) is a vital component of the Internet of Things and smart buildings since it connects millions of autos to improve traffic efficiency. A VANET's peers (mostly automobiles) connect with one another by exchanging, informations on road and circumstances, improving passenger and street safety, and routing traffic through congested areas. Because VANET is so sensitive, it's critical to keep a safe, secure, and attack-free environment that allows for uninterrupted data transmission. However, in this paper, we employ blockchain and software defined networks (SDN) to more effectively and efficiently administer and control VANETs. It helps to lessen the strain on the controller by distributing management work between the blockchain and the SDN due to the ubiquitous processing that occurs. We have evaluated our model with some of deep learning models such as LSTM, DenseNet169, VGG16 and 3D-CNN under performance measures Accuracy, Specificity, Detection Rate, Sensitivity and mostly Security in which our model gives higher satisfaction in terms of accuracy and security.

关键词： Block Chain 5G LSTM Software Defined Networks(SDN) Vehicular Ad-Hoc Network (VANET)

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